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Gong, J., Stanton, B., Lunn, S., Devearus, L., Li, X., Marshall, S., Brathwaite, N.V., Cottrell, L., Harris, C. and Chen, X. (2009) Effects through 24 Months of an HIV/AIDS Prevention Intervention Program Based on Protection Motivation Theory among Preadolescents in the Bahamas. Pediatrics, 123, 917-928.
http://dx.doi.org/10.1542/peds.2008-2363

has been cited by the following article:

• TITLE: Cusp Catastrophe Polynomial Model: Power and Sample Size Estimation

  AUTHORS: Ding-Geng Chen, Xinguang Chen, Feng Lin, Wan Tang, Yuhlong Lio, Yuanyuan Guo

  KEYWORDS: Cusp Catastrophe Model, Polynomial Regression Method, Statistical Power Analysis, Sample Size Determination

  JOURNAL NAME: Open Journal of Statistics, Vol.4 No.10, November 18, 2014

  ABSTRACT: Guastello’s polynomial regression method for solving cusp catastrophe model has been widely applied to analyze nonlinear behavior outcomes. However, no statistical power analysis for this modeling approach has been reported probably due to the complex nature of the cusp catastrophe model. Since statistical power analysis is essential for research design, we propose a novel method in this paper to fill in the gap. The method is simulation-based and can be used to calculate statistical power and sample size when Guastello’s polynomial regression method is used to do cusp catastrophe modeling analysis. With this novel approach, a power curve is produced first to depict the relationship between statistical power and samples size under different model specifications. This power curve is then used to determine sample size required for specified statistical power. We verify the method first through four scenarios generated through Monte Carlo simulations, and followed by an application of the method with real published data in modeling early sexual initiation among young adolescents. Findings of our study suggest that this simulation-based power analysis method can be used to estimate sample size and statistical power for Guastello’s polynomial regression method in cusp catastrophe modeling.